US9063923B2 - Method for identifying the integrity of information - Google Patents
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- US9063923B2 US9063923B2 US12/661,612 US66161210A US9063923B2 US 9063923 B2 US9063923 B2 US 9063923B2 US 66161210 A US66161210 A US 66161210A US 9063923 B2 US9063923 B2 US 9063923B2
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
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- the present invention relates generally to a method for identifying information. More particularly, a novel method for identifying the integrity of information forming at least one of a: single sentence, single phrase, multiple sentences, multiple phrase, information not forming a sentence and not forming a phrase.
- a properly formed query such as “Mary ran quickly and cried” should only retrieve those websites discussing wherein “Mary” is the person who is “crying and running quickly;” however, another query such as “cry ran Mary” which does not form a proper sentence, should imply its search engine to behave or act appropriately or according to its query thus retrieving documents simply comprising the words “cry,” “ran” and “Mary.”
- the search engine or other, can modify its behavior and/or search methodology to inherently and more intuitively match that its query or user.
- the present invention distinguishes over the prior art by providing heretofore a more compelling and effective method for identifying the conceptual and/or grammatical consistency of a data corpus such as a query to optionally manipulate the search behavior or protocols of a search engine and thus better match its user and/or query consistency while providing additional unknown, unsolved and unrecognized advantages as described in the following summary.
- the present invention teaches certain benefits in use and construction which give rise to the objectives and advantages described below.
- the methods and systems embodied by the present invention overcome the limitations and shortcomings encountered when identifying and/or retrieving information.
- the method(s) permits, through the implementation of conceptual associations between word elements of a data corpus, such a CIRN, to identify the conceptual and/or grammatical coherence, integrity and consistency of said data corpus; which may optionally be used to select or choose a particular search behavior that better matches the consistency or integrity of said data corpus.
- a primary objective inherent in the above described methods of use is to provide several methods and systems to identify the conceptual consistency of a data corpus such as a query, thus allowing the method and systems to option to select or choose a search behavior to superiorly match the consistency of said data corpus not taught by the prior arts and further advantages and objectives not taught by the prior art. Accordingly, several objects and advantages of the invention are:
- Another objective is to save user time by providing only conceptually matching data.
- a further objective is to decrease the amount of effort implemented by users to select or modify a particular search behavior or methodology.
- a further objective is to improve the quality and quantity of results.
- a further objective is to permit machines and application the ability of handling natural language more efficiently.
- a further objective is to permit machines and application the ability of identifying natural language more efficiently.
- a further objective is to improve the ability of portable devices to manipulate natural language.
- Another further objective is to encourage users to use natural language when interfacing with machines.
- Another further objective is to allow search engines to behave more intuitively to the user's needs.
- FIG. 1A illustrates an exemplary non-limiting diagram of some steps of the inventive method dealing with a simple sentence such as “Mary ran;”
- FIG. 1B illustrates an exemplary non-limiting diagram of some steps of the inventive method this time dealing with another sentence such as “Mary ran quickly;”
- FIG. 1C illustrates another exemplary non-limiting diagram of some steps of the inventive method this time dealing with a different and more complex sentence such as “silly Mary ran quickly;”
- FIG. 1D illustrates another exemplary non-limiting diagram of some steps of the inventive method this time dealing with a different and more complex sentence such as “silly Mary tall quickly” which according to English Grammar has an incomplete or incorrect grammatical integrity;
- FIG. 1E illustrates another exemplary non-limiting diagram of some steps of the inventive method this time dealing with several sentences such as “silly Mary and tall Lisa;”
- FIG. 1F illustrates yet another exemplary non-limiting diagram of some steps of the inventive method illustrated in FIG. 1A , this time dealing with a sentence of group identifiers (a type of word element) instead of English words, such as “no333 vb777;” which in English, translates and/or represents the sentence “Mary ran;”
- a sentence of group identifiers a type of word element
- FIG. 1G illustrates yet another exemplary non-limiting diagram of some steps of the inventive method illustrated in FIG. 1A , this time dealing with a sentence of eeggis (a type of word element) instead of English words, such as “no3.1 vb7.0;” which in English, translates and/or represents the sentence “Mary ran;”
- FIG. 2 is a non-limiting exemplary diagram of some steps of the inventive method illustrating a network or continuum of word element associations
- FIG. 3A is a non-limiting exemplary diagram of a Data Corpus such as a query with a good grammatical integrity and corresponding selected search behavior;
- FIG. 3B is a non-limiting exemplary diagram of a Data Corpus such as a query with a poor grammatical integrity and corresponding selected search behavior;
- FIG. 4 is a non-limiting block flow diagram of some general and significant steps of the inventive method
- FIG. 5 is an exemplary non-limiting block diagram of some significant steps the inventive method for identifying at least one of a: the number of word elements experiencing associations in a data corpus and/or the number of word elements not experiencing any associations in a data corpus;
- FIG. 6 is an exemplary non-limiting block diagram of the principal steps of one method depicted in FIG. 2 of the disclosed inventive method
- FIG. 7 is yet another variation of some of the steps of the inventive method for identifying the information identifying the grammatical classification of a data corpus.
- FIG. 1A illustrates an exemplary non-limiting diagram of some steps of the inventive method dealing with a simple sentence such as “Mary ran.”
- the Data Corpus 1010 FIG. 1A
- a Conceptual Associative Protocol 1020 FIG. 1A
- CIRN Conceptual Associative Protocol
- the Conceptual Associative Protocol finds or forms an association between “Mary” and “ran.”
- the Associations Table 1050 displays the associations created, found or formed between the words “Mary” and “ran.” For example, in the Associations Table, in the only row, “Mary” under the first column, or Word1, is being associated with “ran” under its corresponding second column, or Word2.
- the Integrity Analysis 1060 FIG. 1A ) inspects and/or analyzes if all (or some) word elements, through their associations, form a line or set or single network of associations that continues.
- the analysis is to inspect if the associations formed involve every single element of the Data Corpus. In such fashion, if there are any word elements left out or that are not part of any associations formed, these word elements can then be used to identify if the data corpus is conceptually, meaningfully and/or grammatical integral, correct or coherent or composed or several corpuses.
- the Before Integrity Analysis 1061 FIG. 1A ) by means of depiction, illustrates the elements of the Data Corpus, under the Words column, with each of their corresponding “tallies” under the Tallies column. As illustrated, neither word has a tally or value in its corresponding tally field. This is because the Integrity Analysis has not yet been performed.
- the After Integrity Analysis 1062 FIG.
- the Integrity Analysis Result 1080 ( FIG. 1A ) displays the sentence “Understanding is good” implying that the Data Corpus is found to follow at least one of the desired (or not) grammatical, conceptual and/or meaningful requirements to be considered “good.”
- FIG. 1B illustrates an exemplary non-limiting diagram of some steps of the inventive method this time dealing with another sentence such as “Mary ran quickly.”
- the Data Corpus 1010 FIG. 1B
- a Conceptual Associative Protocol 1020 FIG. 1B
- CIRN Conceptual Associative Protocol
- the Associations Table 1050 ( FIG. 1B ) displays each of the two associations created, found or formed between their corresponding word elements. For example, in the Associations Table, in the first row, shows that “Mary,” under the first column, or Word1, associates with “ran” under the corresponding second column, or Word2 on its right side. In similar fashion, in the second row, the word “ran” associates with “quickly” to its right. Then, the Integrity Analysis 1060 ( FIG. 1B ) inspects and/or analyzes if all (or some) word elements, through their associations, form a line or set of associations that continues. In other words, the analysis inspects if the associations formed involve every single element of the Data Corpus.
- the Before Integrity Analysis 1061 ( FIG. 1B ) by means of depiction, illustrates all the elements of the Data Corpus, under the Words column, with each of their corresponding “tallies” under the Tallies column. As illustrated, neither of the three words has a tally or value in their corresponding tally fields. This is because the Integrity Analysis has not yet been performed.
- the After Integrity Analysis 1062 ( FIG. 1B )
- the tally or value “T” is used in this example to identify all those words that are involved on a particular or given association.
- the word “Mary” shows a tally or value “T”
- the word “ran” shows another tally or value “T”
- the word “quickly” also shows a tally or value “T” meaning and/or indicating that all three words are indeed part of an association illustrated in the Associations Table.
- the Integrity Analysis Result 1080 ( FIG. 1B ) displays the said outcome of analyzing such as Data Corpus by displaying the sentence “Understanding is good;” which obviously implies that “Mary ran quickly” is found to be at least one of a: grammatical correct, conceptual proper and/or meaningfully accurate or ultimately “good.”
- FIG. 1C illustrates another exemplary non-limiting diagram of some steps of the inventive method this time dealing with a different and more complex sentence such as “silly Mary ran quickly.”
- the Data Corpus 1010 FIG. 1C
- a Conceptual Associative Protocol 1020 FIG. 1C
- CIRN Conceptual Associative Protocol
- the Associations Table 1050 displays each of the three associations created, found or formed between their corresponding word elements. For example, in the Associations Table, in the first or top row, shows the word “silly,” under the first column or Word1, being associated with the word “Mary,” under the second column, or Word2. Also in the Associations Table, the word “Mary” is associated with “ran” in the middle or second row. Finally, in the last or third row, the word “ran” associates with “quickly.”
- the Integrity Analysis 1060 FIG.
- the Before Integrity Analysis 1061 ( FIG. 1C ) by means of depiction, shows the word elements of the Data Corpus before they are analyzed.
- this table illustrates all the elements of the Data Corpus, under the Words column, with each of their corresponding “tallies” under the Tallies column with no tallies or values assigned yet.
- the After Integrity Analysis 1062 FIG. 1C , by means of depiction, shows the resulting tallies or involvement that every word of the data corpus experiences through associations that were formed by the Conceptual Associative Protocol. For example, the tally or value “T” is used in this example to identify every word involved in a particular or given association.
- the word “silly” shows or has a “T” in its tally field
- the word “Mary” shows a tally or value “T”
- the word “ran” shows another tally or value “T”
- the word “quickly” also shows a tally or value “T” meaning and/or indicating that all four words are indeed part of an association as illustrated in the Associations Table.
- the Data Corpus or “silly Mary ran quickly” is considered to be “understandably good or proper.”
- FIG. 1D illustrates another exemplary non-limiting diagram of some steps of the inventive method this time dealing with a different and more complex sentence such as “silly Mary tall quickly” which according to English Grammar has an incomplete or incorrect grammatical integrity.
- the Data Corpus 1010 FIG. 1D
- a Conceptual Associative Protocol 1020 FIG. 1D
- CIRN Conceptual Associative Protocol
- the Associations Table 1050 ( FIG. 1D ) only displays a single association between the word elements “silly” and “Mary” in the only displayed row. In this association, the word “silly,” under the first column or Word1, is being associated with the word “Mary,” under the second column, or Word2. Consequentially, many word elements of the Data Corpus did not experience an association or failed to associate with each other.
- the Integrity Analysis 1060 ( FIG.
- the Before Integrity Analysis 1061 ( FIG. 1D ) by means of depiction, shows the word elements of the Data Corpus before they are analyzed.
- this table illustrates all the elements of the Data Corpus, under the Words column, with each of their corresponding “tallies” under the Tallies column having no tallies or values assigned yet.
- the After Integrity Analysis 1062 FIG. 1D , by means of depiction, shows the resulting tallies or the involvement that every word of the data corpus experiences through associations that were formed by the Conceptual Associative Protocol. For example, the tally or value “T” is used in this example to identify every word involved in a particular or given association. Consequentially, only the words “silly” and “Mary” show or have a “T” in their tally fields.
- FIG. 1E illustrates another exemplary non-limiting diagram of some steps of the inventive method this time dealing with several sentences such as “silly Mary and tall Lisa.”
- this data corpus is comprised of two sentences.
- the Data Corpus 1010 FIG. 1E
- a Conceptual Associative Protocol 1020 FIG. 1E
- CIRN Conceptual Associative Protocol
- the Associations Table 1050 ( FIG. 1E ) displays two associations; wherein the first association (first row) involves the word elements “silly” and “Mary” and the second association (second row) involves the word elements “tall” and “Lisa.”
- the Integrity Analysis 1060 ( FIG. 1E ) inspects and/or analyzes the Data Corpus' integrity or if all (or some) word elements, through their associations, form a line or set of associations that continues.
- the analysis inspects if all the word elements of the Data Corpus are involved in at least one association, which also means, that the analysis is identifying if any word elements are left unassociated in the Data Corpus. In such fashion, if there any word elements which are left out or do not form part of any of the associations formed, they can then be used to identify the conceptual integrity, grammatical integrity, meaningful integrity, other type of integrity and/or to identify if the data corpus is comprised of several data corpuses, such as several sentences.
- the Before Integrity Analysis 1061 FIG. 1E by means of depiction, shows the word elements of the Data Corpus before they are analyzed.
- this table illustrates all the elements of the Data Corpus, under the Words column, with each of their corresponding “tallies” under the Tallies column having no tallies or values assigned yet.
- the After Integrity Analysis 1062 FIG. 1E , by means of depiction, shows the resulting tallies or the involvement that every word of the data corpus experiences through associations that were formed by the Conceptual Associative Protocol. For example, the tally or value “T” is used in this example to identify every word involved in a particular or given association. Consequentially, only the words “silly,” “Mary,” “tall” and “Lisa” show or have a “T” in their corresponding tally fields.
- the word “and” shows or has no tally or value “T.” This is because the word “and” did not form or experience any associations as illustrated in the Association Table.
- the word “and” can be specifically used to separate or identify different regions or sections of information. Accordingly, the word “and” in this particular case, is separating two sentences or phrases. As a result, although the word “and” experienced no associations, it may be ignored or used to separate/identify different data corpuses or different networks of associations implying several sentences.
- the Data Corpus or “silly Mary and tall Lisa” is said to have a “good” grammatical (or any other type) integrity and, in fact, be comprised of as in this example, of two sentences or phrases which are separated by the word “and.”.
- the Integrity Analysis Result 1080 ( FIG. 1E ) displays the said outcome of analyzing said Data Corpus announcing or displaying “Understanding is good;” which obviously implies that “silly Mary and tall Lisa” is found, at least with the chosen associative analysis criteria, to be grammatical correct, proper and/or accurate (good).
- FIG. 1F illustrates yet another exemplary non-limiting diagram of some steps of the inventive method illustrated in FIG. 1A , this time dealing with a sentence of group identifiers (a type of word element) instead of English words, such as “no333 vb777;” which in English, translates and/or represents the sentence “Mary ran.”
- the Data Corpus 1010 FIG. 1F
- a Conceptual Associative Protocol 1020 FIG.
- the Conceptual Associative Protocol finds that “no333” (Mary) associates to “vb777” (ran).
- the Associations Table 1050 FIG. 1F ) displays the formed association; wherein the word element or group identifier “no333” associates with “vb777.”
- the Integrity Analysis 1060 FIG. 1F
- 1F inspects and/or analyzes the Data Corpus' integrity or if all (or some) word elements, through their associations, form a line or set of associations that continues.
- the analysis inspects if all group identifiers of the Data Corpus are involved in at least one association, which also means, that the analysis is identifying if any group identifier is left unassociated in the Data Corpus.
- the said unassociated group identifier can then be used to identify the conceptual integrity, grammatical integrity, meaningful integrity and/or other type of integrity of the data corpus.
- FIG. 1F by means of depiction, shows the word elements of the Data Corpus before they are analyzed. As depicted, this table illustrates all group identifiers of the Data Corpus, under the Identifier column, with each of their corresponding “tallies” under the Tallies column having no tallies or values assigned yet.
- the After Integrity Analysis 1062 FIG. 1F , by means of depiction, shows the resulting tallies or the involvement that every group identifier of the data corpus experiences through associations that were formed by the Conceptual Associative Protocol. For example, the tally or value “T” is used in this example to identify every word involved in a particular or given association.
- 1F displays the said outcome of analyzing said Data Corpus announcing or displaying “Understanding is good;” which obviously implies that “no333 vb777” (Mary ran) is found, at least with the chosen associative analysis criteria, to be grammatical correct, proper and/or accurate (good).
- FIG. 1G illustrates yet another exemplary non-limiting diagram of some steps of the inventive method illustrated in FIG. 1A , this time dealing with a sentence of eeggis (a type of word element) instead of English words, such as “no3.1 vb7.0;” which in English, translates and/or represents the sentence “Mary ran.”
- the Data Corpus 1010 FIG. 1G
- a Conceptual Associative Protocol 1020 FIG.
- 1G inspects and/or analyzes the Data Corpus' integrity or if all (or some) eeggis, through their associations, form a line or set of associations that continues.
- the analysis inspects if all eeggis of the Data Corpus are involved in at least one association, which also means, that the analysis is identifying if any eeggi is left unassociated. In such fashion, if there any eeggi left out or that it does not form part of any of the association, the said unassociated eeggi can then be used to identify the conceptual integrity, grammatical integrity, meaningful integrity and/or other type of integrity or coherence of the data corpus.
- the Before Integrity Analysis 1061 FIG.
- FIG. 1G by means of depiction, shows the word elements of the Data Corpus before they are analyzed. As depicted, this table illustrates all group identifiers of the Data Corpus, under the Identifier column, with each of their corresponding “tallies” under the Tallies column having no tallies or values assigned yet.
- the After Integrity Analysis 1062 FIG. 1G , by means of depiction, shows the resulting tallies or the involvement that every eeggi of the data corpus experiences through associations formed by the Conceptual Associative Protocol. For example, the tally or value “T” is used in this example to identify every eeggi involved in a particular or given association.
- 1G displays the said outcome of analyzing said Data Corpus announcing or displaying “Understanding is good;” which obviously implies that “no3.1 vb7.0” (Mary ran) is found, at least with the chosen associative analysis criteria, to be grammatically correct, proper and/or accurate (good).
- FIG. 2 is a non-limiting exemplary diagram of some steps of the inventive method illustrating a network or continuum of word element associations.
- the Data Corpus 2010 ( FIG. 2 ) comprises the long sentence “silly Mary, tall Lisa and cute Gina are running.”
- the Graphical Network Diagram 2020 ( FIG. 2 ) depicts the associations resulting from the said Data Corpus.
- the word “silly” 2021 ( FIG. 2 ) is associated to the word “Mary” 2022 ( FIG. 2 ).
- the word “tall” 2023 ( FIG. 2 ) is associated to the word “Lisa” 2024 ( FIG. 2 ).
- the word “cute” 2025 ( FIG. 2 ) is associated to the word “Gina” 2026 ( FIG.
- FIG. 3A is a non-limiting exemplary diagram of a Data Corpus such as a query with a good grammatical integrity and corresponding selected search behavior.
- the Data Corpus 3010 FIG. 3A
- the Integrity Analysis 3060 FIG. 3A
- the Integrity Analysis Result 3080 FIG. 3A
- the Search Behavior 3090 ( FIG. 3A ) is selected or chosen for performing a search corresponding to the integrity of its query.
- FIG. 3B is a non-limiting exemplary diagram of a Data Corpus such as a query with a poor grammatical integrity and corresponding selected search behavior.
- the Data Corpus 3010 FIG. 3B
- the Integrity Analysis 3060 FIG. 3B
- the Integrity Analysis Result 3080 FIG. 3B
- the corresponding Search Behavior 3090 is selected or chosen for performing a search corresponding to the integrity of its query, such as a text-based search comprising results with the words of the query in randomly isolated order.
- FIG. 4 is a non-limiting block flow diagram of some general and significant steps of the inventive method.
- the initial or First Step 4010 ( FIG. 4 ) involves the obvious step of identifying a data corpus comprising several word elements. For example, identifying the word elements in a query or other.
- the next or Second Step 4020 ( FIG. 4 ) involves the step of identifying at least one association between several of said word elements such as implementing a word element associative analysis such a CIRN. For example, through an associative formation or identification analysis such as CIRN, particular sets of associations between the word elements of the Data Corpus can be found, identified or formed.
- the next or Third Step 4030 involves the step of performing an analysis involving at least one of a: identifying an N number of word elements being associated and identifying an M number of word elements not being associated. For example, in this step, the number of word elements, such as M, belonging to an association and/or the number of word elements, such as N, not belonging to any association from the Data Corpus are identified.
- the next or Fourth Step 4040 FIG. 4
- FIG. 5 is an exemplary non-limiting block diagram of some significant steps the inventive method for identifying at least one of a: the number of word elements experiencing associations in a data corpus and/or the number of word elements not experiencing any associations in a data corpus.
- the First Step 5010 ( FIG. 5 ) involves the step of identifying at least one association between several word elements of a data corpus. For example, in this initial step, one or several associations involving several word elements of a data corpus are identified.
- the next or Second Step 5020 ( FIG. 5 ) involves the step of implementing an identifying information such as a tally for identifying at least one of a: all word elements involved or belonging to said association and all word elements not involved or belonging to any association.
- a tally (an identifying information) is used to identify all and every word element involved and/or belonging to the association and/or alternatively a tally may also be used for identifying all those other word elements not involved or experiencing any type of association. In such fashion, the word elements experiencing associations can be differentiated from those not experiencing any form of associations.
- the next or Third Step 5030 involves the step of identifying the number of word elements identified by said identifying information such as tally. For example, this step implies the step of counting the word elements that were tallied, thus identifying the number of associated and/or non-associated word elements on the data corpus.
- the final or Fourth Step 5040 involves the obvious step of implementing said identified number for identifying at least one of a said: number of word elements involved in a association and number of word elements not involved in any association implementing said counted identifying information such as tally number.
- this obvious step involves the step of using, registering or implementing the count or number of word elements involved in association and/or not involved in any association to identify how many word elements from the total word elements of the data corpus form part (or not form part) belonging to associations.
- an inventory can be made of how many word elements of a data corpus belong to associations and/or how many word elements of a data corpus do not belong to any associations.
- FIG. 6 is an exemplary non-limiting block diagram of the principal steps of one method depicted in FIG. 2 of the disclosed inventive method.
- the First Step 6010 ( FIG. 6 ) involves the step of identifying a word element from an association. For example, from an association between “silly” and “Mary,” the word “silly” is selected or identified.
- the next or Second Step 6020 ( FIG. 6 ) involves the step of identifying a different word element from the previous association. For example, from the association mentioned in the previous step (“Mary” and “silly”), “silly” was selected, in this step, the different word of the association or “Mary” is identified or selected.
- the next or Third Step 6030 ( FIG.
- the Fourth Step 6040 ( FIG. 6 ) involves the steps of repeating the Second Step and the Third Step until every remaining different association and different word element is identified. For example, in the previous steps, “Mary” was used to identify other associations and therefore other words. This step involves using the others words associated to “Mary” and their additional associations to continue identifying yet other elements through more associations. In such fashion, associations among the associations is analyzed or inspected.
- FIG. 7 is yet another variation of some of the steps of the inventive method for identifying the information identifying the grammatical classification of a data corpus.
- the First Step 7010 ( FIG. 7 ) involves the step of identifying a network of at least one association between several word elements of a data corpus. For example, identifying all the associations and/or their integrity of the word elements of a data corpus.
- the next or Second Step 7020 ( FIG. 7 ) involves the step of identifying a grammatical inventory including at least one of a: number of subjects, number of objects, number of verbs, number of adjectives, number of adverbs, number of nouns, number of articles, number of conjunctions and number of functional words.
- this step involves identifying the several numbers of word elements, specifically in a grammatical sense, involved in the associations forming a network of associations, such as counting the number of acting noun or subjects, counting the number of acting or main verbs, identifying the number or adverbial sentences or others.
- the Third Step 7030 ( FIG. 7 ) involves the step of implementing said grammatical inventory for identifying at least one of a: sentence, phrase and nonsensical corpus. For example, in this step the presence or existence of the grammatical essence of the word elements could be used to determine if a data corpus is indeed a sentence, a phrase or other.
- the last of Fourth Step 7040 involves the obvious step of identifying said data corps to be at least one of a: sentence, phrase and nonsensical corpus. For example, once it has been determined that a data corpus has all the elements and association to be considered a phrase instead of a sentence, then identifying the analyzed data corpus as a phrase. In similar fashion, if the corpus lacks the required element to be considered a phrase or a sentence, then identified the analyzed data corpus as a nonsensical corpus per se.
- the described methods overcomes the limitations encountered by current information technologies such as search engines, speech recognition, word processors, and others which fail to identify the integrity of a data corpus; which potentially leads to the use of implementing keywords and randomly isolated words responsible for generating irrelevant data, irrational data and user confusion.
- the described inventive methods allow current and future information technologies to properly and effectively identify the integrity of information while acknowledging said integrity to users thus enforcing better communications and language between users and machines and applications.
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US9858336B2 (en) | 2016-01-05 | 2018-01-02 | International Business Machines Corporation | Readability awareness in natural language processing systems |
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US20100241419A1 (en) | 2010-09-23 |
US20100241630A1 (en) | 2010-09-23 |
US20100241631A1 (en) | 2010-09-23 |
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